The pre-dominant approach to language modeling to date is based on recurrent neural
networks. Their success on this task is often linked to their ability to capture unbounded …

RNNs have been shown to be excellent models for sequential data and in particular for data
that is generated by users in an session-based manner. The use of RNNs provides …

从特征传输方式, 空间维度, 特征维度3 个角度, 论述了近年来卷积神经网络结构的改进方向,
介绍了卷积层, 池化层, 激活函数, 优化算法的工作原理, 从基于值, 等级, 概率和转换域四大类 …

Due to the trending need of building autonomous robotic perception system, sensor fusion
has attracted a lot of attention amongst researchers and engineers to make best use of cross …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

We propose an approximate strategy to efficiently train neural network based language
models over very large vocabularies. Our approach, called adaptive softmax, circumvents …

Most of the existing Neural Machine Translation (NMT) models focus on the conversion of
sequential data and do not directly use syntactic information. We propose a novel end-to …

There has been relatively little attention to incorporating linguistic prior to neural machine
translation. Much of the previous work was further constrained to considering linguistic prior …

Neural language models predict the next token using a latent representation of the
immediate token history. Recently, various methods for augmenting neural language models …

The Softmax function is used in the final layer of nearly all existing sequence-to-sequence
models for language generation. However, it is usually the slowest layer to compute which …